Comprehensive comparison for Quantization technology in AI applications

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import bitsandbytes as bnb
from typing import Optional, Dict, Any
import logging
from dataclasses import dataclass

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

@dataclass
class ModelConfig:
    model_name: str = "meta-llama/Llama-2-7b-chat-hf"
    load_in_4bit: bool = True
    bnb_4bit_compute_dtype: str = "float16"
    bnb_4bit_quant_type: str = "nf4"
    use_nested_quant: bool = True
    max_memory_mb: int = 8000

class QuantizedModelService:
    def __init__(self, config: ModelConfig):
        self.config = config
        self.model = None
        self.tokenizer = None
        
    def initialize(self) -> bool:
        try:
            logger.info(f"Loading model {self.config.model_name} with 4-bit quantization")
            
            bnb_config = BitsAndBytesConfig(
                load_in_4bit=self.config.load_in_4bit,
                bnb_4bit_compute_dtype=getattr(torch, self.config.bnb_4bit_compute_dtype),
                bnb_4bit_quant_type=self.config.bnb_4bit_quant_type,
                bnb_4bit_use_double_quant=self.config.use_nested_quant
            )
            
            self.model = AutoModelForCausalLM.from_pretrained(
                self.config.model_name,
                quantization_config=bnb_config,
                device_map="auto",
                trust_remote_code=True,
                max_memory={0: f"{self.config.max_memory_mb}MB"}
            )
            
            self.tokenizer = AutoTokenizer.from_pretrained(
                self.config.model_name,
                trust_remote_code=True
            )
            
            if self.tokenizer.pad_token is None:
                self.tokenizer.pad_token = self.tokenizer.eos_token
            
            logger.info("Model loaded successfully")
            logger.info(f"Memory footprint: {self.model.get_memory_footprint() / 1e9:.2f} GB")
            return True
            
        except Exception as e:
            logger.error(f"Failed to initialize model: {str(e)}")
            return False
    
    def generate_response(self, prompt: str, max_new_tokens: int = 256) -> Optional[Dict[str, Any]]:
        if self.model is None or self.tokenizer is None:
            logger.error("Model not initialized")
            return None
        
        try:
            inputs = self.tokenizer(
                prompt,
                return_tensors="pt",
                padding=True,
                truncation=True,
                max_length=512
            ).to(self.model.device)
            
            with torch.inference_mode():
                outputs = self.model.generate(
                    **inputs,
                    max_new_tokens=max_new_tokens,
                    temperature=0.7,
                    do_sample=True,
                    top_p=0.9,
                    pad_token_id=self.tokenizer.pad_token_id
                )
            
            response_text = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
            
            return {
                "success": True,
                "response": response_text,
                "tokens_generated": len(outputs[0]) - len(inputs.input_ids[0])
            }
            
        except torch.cuda.OutOfMemoryError:
            logger.error("GPU out of memory during generation")
            torch.cuda.empty_cache()
            return {"success": False, "error": "Out of memory"}
        except Exception as e:
            logger.error(f"Generation failed: {str(e)}")
            return {"success": False, "error": str(e)}

if __name__ == "__main__":
    config = ModelConfig()
    service = QuantizedModelService(config)
    
    if service.initialize():
        result = service.generate_response("What is machine learning?")
        if result and result.get("success"):
            print(f"Response: {result['response']}")
            print(f"Tokens: {result['tokens_generated']}")